Narrative comprehension and production abilities of children with 22q11.2 deletion syndrome
Bibliographic record
Abstract
BACKGROUND: The 22q11.2 Deletion Syndrome (22q11DS) is associated with language deficits and weak intellectual functioning. In other clinical groups, linguistic and cognitive difficulties have been associated with impaired acquisition of narrative abilities. However, little is known about the narrative abilities of children with 22q11DS. AIMS: To describe the ability of children with 22q11DS to produce and comprehend narrative macrostructure. Additionally, to examine the role of intellectual functioning in explaining their narrative difficulties. METHODS AND PROCEDURES: Narrative skills of 14 school-aged children with 22q11DS were compared to those of younger typically developing (TD) children matched on mental age and same-aged peers with Developmental Language Disorder (DLD). OUTCOMES AND RESULTS: Children with 22q11DS had significantly lower scores on narrative comprehension than younger TD children. No significant differences emerged on narrative production. Children with 22q11DS and children with DLD did not differ significantly on any of the narrative measures. CONCLUSIONS AND IMPLICATIONS: Narrative comprehension in children with 22q11DS seems more affected than production. Narrative comprehension difficulties cannot be entirely explained by a low level of intellectual functioning. Narrative comprehension and production abilities in 22q11DS require further consideration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".